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Evaluating the accuracy of HMM-based and SVM-based spotters in detecting keywords and recognizing the true place of keyword occurrence shows that the HMM-based spotter detects the place of occurrence more precisely than the SVM-based spotter. On the other hand, the SVM-based spotter performs much better in detecting
for handwritten historic documents can fill this gap. However, most such systems have trouble with the great variety of writing styles. It is not uncommon for handwriting processing systems to be built for just a single book. In this paper we show that neural network based keyword spotting systems are flexible enough to
loss function during training is that it aims at maximizing not only the relative ranking scores, but also adjusts the system to use a fixed threshold and thus maximizes the detection accuracy rates. We use the new loss function in the structured prediction setting and extend the discriminative keyword spotting algorithm
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